There are several survey papers that have extensively discussed model-based localization and mapping approaches. The development of SLAM problem in early decades has been well summarized in [1]}, [2]}. The seminal survey [3]} provides a thorough discussion on existing SLAM work, reviews the history of development and charts several future directions. Although this paper contains a section which briefly discusses deep learning models, it does not overview this field comprehensively, especially due to the explosion of research in this area of the past five years. Other SLAM survey papers only focus on individual flavours of SLAM systems, including the probabilistic formulation of SLAM [4]}, visual odometry [5]}, pose-graph SLAM [6]}, and SLAM in dynamic environments [7]}.
We refer readers to these surveys for a better understanding of the conventional model based solutions.
On the other hand, [8]} has a discussion on the applications of deep learning to robotics research; however, its main focus is not on localization and mapping specifically, but a more general perspective towards the potentials and limits of deep learning in a broad context of robotics, including policy learning, reasoning and planning.
